Integrating deep learning and critical slowing theory for automated epileptic detection and seizure prediction

Key Points

• Automated detection of seizures can enhance real-time monitoring of epilepsy, enabling timely interventions.
• During testing, the model achieved a sensitivity of 92% in predicting seizures, emphasizing its clinical potential.
• Integration of deep learning algorithms with critical slowing theory provides a novel mechanism for predicting epileptic events.
• This approach may enable broader applications in neurology, requiring further validation in diverse epilepsy populations.